Few Shot Learning

Few Shot Learning (FSL) is a type of Machine Learning where the Training Set has limited examples. (The Machine is expected to learn something from just a few examples.) Few Shot Learning is used in Computer Vision, Robotics, Natural Language Processing, Acoustic Signal Processing, and much more.  In a nutshell, FSL trains a function to predict similarity. When performing few-shot learning, the prediction accuracy depends on the number of ways and the number of shots. As the number of ways increases, the prediction accuracy drops.